Advancing Security with Digital Twins: A Comprehensive Survey

TL;DR

This survey reviews how digital twins are used to enhance security across various electronic systems, highlighting recent implementations, challenges, and future research directions in this emerging field.

Contribution

It provides a comprehensive overview of digital twin-based security solutions across multiple domains, unifying diverse approaches and exploring integration with large language models.

Findings

Digital twins enable backward traceability and continuous verification.

They are effective in detecting counterfeit electronics and intrusion attempts.

Integration with AI enhances security capabilities.

Abstract

The proliferation of electronic devices has greatly transformed every aspect of human life, such as communication, healthcare, transportation, and energy. Unfortunately, the global electronics supply chain is vulnerable to various attacks, including piracy of intellectual properties, tampering, counterfeiting, information leakage, side-channel, and fault injection attacks, due to the complex nature of electronic products and vulnerabilities present in them. Although numerous solutions have been proposed to address these threats, significant gaps remain, particularly in providing scalable and comprehensive protection against emerging attacks. Digital twin, a dynamic virtual replica of a physical system, has emerged as a promising solution to address these issues by providing backward traceability, end-to-end visibility, and continuous verification of component integrity and behavior. In this paper, we comprehensively present the latest digital twin-based security implementations, including their role in cyber-physical systems, Internet of Things, cryptographic systems, detection of counterfeit electronics, intrusion detection, fault injection, and side-channel leakage. This work considers these critical security use cases within a single study to offer researchers and practitioners a unified reference for securing hardware with digital twins. The paper also explores the integration of large language models with digital twins for enhanced security and discusses current challenges, solutions, and future research directions.